Supplementary Material - Base model assumptions

Introduction

Methods

Example interpretation of EE

How to interpret results

Results

Body Weight

Change in Male Body Weight input assumptions

Changes in base model assumptions for body weight alters how change in energy imbalance impacts BMI category prevalence estimates. The figure below shows the elementary effect for each local sensitivity analysis change Male assumptions body weight assumptions.

  • Changes in the underweight and healthy weight body weight assumptions reducing the sensitivity of the model, where higher assumed body weight reduces the flow into overweight creating a lower overweight BMI % and higher underweight/ health weight BMI %.
  • Similarly, higher body weight for obesity cohorts reduces the prevalence at the of the model, and increases the prevalence for overweight outcomes.
  • Younger ages impact the BMI outcomes for all older age groups, with the largest impact on the age groups being altered.
  • Older age groups do not impact younger outcomes.
  • Changes in each BMI categories mainly impacts the outcome of the BMI category being changed and the neighboring BMI category.
    • Assumptions in Healthy weight impacts the underweight and healthy weight and overweight outcome, however has minimal impact on with obesity outcome.
    • Change in the assumptions for overweight main impact the outcome for all BMI category
    • Changing the Obesity assumptions change the obesity and overweight outcomes

Change in Female Body Weight input assumptions

There are similar relationships as noted in the male assumptions.

Additionally;

  • The results show that the intergenerational effects.
    • Changes in the female assumptions has a slight impact in younger male age group.
  • Larger changes in female outcomes, results in lager impacts in younger age groups.
    • Changes in the high fertility age groups results in proportionally higher change in younger age groups.
      • Change in body weight assumptions for Age 2 peaked at average slope of 15 %/kg resulting in 0.5 %/kg changes in young males, 3.3% of the effect was transferred.
      • Change in body weight assumptions for Age 20 24 peaked at 1 %/kg resulting in a 0.1%/kg change in young males, 10% of the effect was transferred.

Height

Similar to changes in the assumed body weight for each cohort, change in height, impacts the how influential energy surplus and deficits are resulting in changes in the flows between BMI categories.

Change in Male height input assumptions

Change in Female height input assumptions

Growth Function kJ/day

Changes in the assumption Kj/day needed for growth resulting in relatively small changes in the BMI outcomes. The largest impact occurred in adolescent age groups where the growth assumptions where the largest. The larger observed impact in males was in 9-11 year olds and 12-15 year olds, where on average 1 Kj resulted in 0.2% change in the outcome.

Macro nutrients Kj per gram

Each of the food groups are broken down to the macro nutrients; carbohydrate, protein, fats and sugars. The energy from a gram of macronutrients is an model input. Since the input energy assumption for each macronutrient effect the energy for each food group to each age-gender-BMI group, all of the population. This creates highlight sensitivity assumptions.

  • The input of macronutrient becomes more sensitive for older age group, this is due the the cumulative impact over the populations life course.
  • Higher kJ/g leads to higher daily total dietary intake leading to higher prevalence of overweight and obesity.
  • The lowest impact to overweight or obesity was 0.8%/kJ.
  • The higher impact was 19.89%/kJ.

Example of interpretation

Thermic effect of food (TEF)

The themogenesis effect of food (TEF), is the proportion of the energy used for digestion. A higher TEF means less of the dietary energy is available after digestion. TEF input assumptions impacts whole population, translating to a highly sensitivity input assumption.

  • Change in TEF, cumulative over the life course leading to a higher impact for older age groups.
  • 1 unit change in the TEF of carbohydrate results in a between 26.18 to 488.39 % change in prevalence of underweight and healthy weight.
    • Since TEF unit is a proportion, 1 unit change is too wide, a 0.1 unit change would result in a 2.6% to 48.84% change in the prevalence of underweight.
  • 1 unit change in the TEF of Fat results in a between 29.54 to 545.79 % change in prevalence of underweight and healthy weight.
    • a 0.1 unit change would result in a 2.95% to 54.58% change in the prevalence of underweight.
  • 1 unit change in the TEF of Protein results in a between 14.22 to 345.20 % change in prevalence of underweight and healthy weight.
    • a 0.1 unit change would result in a 1.42% to 34.52% change in the prevalence of underweight.
  • 1 unit change in the TEF of Sugar results in a between 38.45 to 533.02 % change in prevalence of underweight and healthy weight.
    • a 0.1 unit change would result in a 3.85% to 53.30% change in the prevalence of underweight.

Example of interpretation

Adult to Adult Social Transmission

Adult to Adult social transmission assumptions impact how changes in physical activity (PAL) and dietary behaviors are transmitted to other adults through role-modelling. This assumptions has little impact in the output, however, because this variable is depended on change of behaviour, role-modelling likely interactions with interventions effects.

Adult to Child Social Transmission

Similar to “Adult to Adult” role-modelling, there is little impact in BMI outcome when Adult to Child role-modelling assumptions are varied.

Child to Child Social Transmission

Similar to “Adult to Adult” role-modelling, there is little impact in BMI outcome when Child to Child role-modelling assumptions are varied. It possbile that these assumptions ill interact with interventions assumptions.

Infant Reported behaviours

The assumed reported infant behaviours exemplifies the intergenerational effects. However these impacts are minimal.

  • The proportion of mother that breast feed impact older age groups, this is because of the increase of energy expenditure caused by breastfeeding.
  • The proportion of infants that either consume non-core foods and TV for greater then 1 hr/day impact younger age groups, higher non-core food consumption and greater TV viewing increases the prevalence of overweight and obesity.

Intergenerational OR effects

Mortality ratios

Hazard ratios are applied to exogenous mortality rate so predicted population dynamics are maintained.

  • Changing these assumptions impact older age group, primarily cause by higher mortality rates in these age groups.
  • Since the primary outcome is a percentage, these ratio have little impact.

METs

Duration and intensive (METs) of physical activity are both used in estimating total energy expenditure. The assumed METs for each of the movement categories is applied to all of the population, which results in highligh sensitivity input assumptions.

  • Impact from variations in METs assumptions accumulate over the life cource of the population resulting in higher impacts for older age groups.
  • Low MET values for sleep and inactivity aswell as high duration results in high sensitivity.
    • 1 unit increase in sleep METs results in a between 20.42% to 312.38% change in obesity.
    • 1 unit increase in inactive METs results in a between 12.5% to 206.59% change in obesity.
  • Other results;
    • 1 unit increase in screen time METs results in a between 8.76% to 163.84% change in obesity.
    • 1 unit increase in light physical activity METs results in a between 4.18% to 74.62% change in obesity.
    • 1 unit increase in moderate physical activity METs results in a between 2.97% to 33.48% change in obesity.
    • 1 unit increase in moderate physical activity METs results in a between 1.88% to 29.19% change in obesity.

LIGHT PA

Each behaviour is structure so that they are age dependent, observed surveyed behaviours were modelled using a linear regression with age groups as the independnet variable. This creates two variables for each behaviour, the intercept is the level of behaviour at the youngest age group (2 years old) and the change of behaviours over the life course.

  • Changes in the intercept assumptions have cumulative impacts over the life course, resulting in higher impacts in older age groups compared to younger age groups.
  • Variying age-on-age (AGE SLOPE) change the trajectory of behaviours over the life course, making older age group BMI outcomes sensitivity to changes in age slope.
  • For each minute increase in the light physical activity intercept prevalence can change 0.5%.
    • the input assumptions range between 90 mins and 127 mins; a change in 10 mins results in 5% change in outcome.
  • For each minute increase in the light physical activity age slope prevalence can change 0.8%.
    • the input assumptions range between 13 mins and 36 mins per age group; a change in 5 mins results in 4% change in outcome.

MODERATE PA

VIGOROUS PA

SCREEN TIME

SLEEP

Fruit Reported Intake

Vegetables Reported Intake

Grains Reported Intake

Dairy Reported Intake

Meat and Protein Reported

Discretionary foods Reported

Fats and Oils Reported

Sugar based beverage Reported

Water Reported

Initial FM %

Change in Male height input assumptions

Change in Female height input assumptions

Proportion of nutrients within food group Inputs

Grains

Vegetables

Fruit

Dairy

Meat

Fats

Discretionary foods

Miscellaneous (Other)

Non-sugar-sweetened beverages

Sugar-sweetened beverages

Initial BMI Prevalence Inputs

Change in Male Initial BMI Prevalence

Change in Female Initial BMI Prevalence